Using telerehabilitation for the initial remote evaluation and diagnosis of musculoskeletal disorders: perceptions of patients and health care providers
Bibliographic record
Abstract
To explore, using semi-structured interviews, the perceptions of patients, clinicians and other stakeholders involved in telehealth regarding the perceived strengths and limitations of an initial remote evaluation for the diagnosis of MSKDs and the current environment of telehealth in the province of Quebec, Canada. The identified barriers, facilitators, and needs of patients and health care providers are important to acknowledge as they need to be taken into account to allow the development of effective strategies to implement remote evaluation to assess new patients with MSKDs. The main themes were: 1- Several participants felt that telehealth, including a remote evaluation, is a solution to improve access to care by reducing travel, wait times and time away from usual activities. 2- Patients and health care providers reported that a remote evaluation was more suitable for simple MSKD presentations. 3- Some clinicians expressed concerns about the potential increase in diagnostic errors and the feasibility of performing all usual components of a MSK physical examination. 4- Patients expressed doubts regarding their ability to perform some of the tasks or tests on themselves during the remote MSK physical examination and 5- More common issues related to the use of telerehabilitation were also highlighted by all such as the impact on the patient-clinician relationship, access to adequate hardware, general digital literacy, data management and issues with confidentiality. The perceived challenges of remote evaluation, specifically those related to the remote physical examination and the increased risk of diagnostic errors, must be weighed against the opportunities for improving access to care. Providing specific training for health care providers and patients on the proper use of telehealth, including how to perform a remote physical examination and how to communicate and collaborate effectively in a virtual setting, is essential to facilitate successful remote evaluation. Semi-structured interviews were conducted in the province of Quebec with patients (n=11), health care providers (physiotherapists [n=10] and physicians [n=11]), telehealth software specialists (n=2) and regulatory organizations (n=4). Five interview guides, adapted to the different types of participants, were developed using the Consolidated Framework of Implementation Research and the framework of Mathieu-Fritz et al. for the study of telehealth interventions. The themes explored included participants’ previous experiences with telehealth, their perceived strengths and limitations of telehealth, particularly regarding the initial evaluation and diagnosis of new patients, and the current global environment of telehealth use. All semi-structured interviews were transcribed and inductive thematic analysis was performed. The framework of Mathieu-Fritz et al. was used as the template for analyses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".